I think the evolutionary models are the key. Start with a simple learning model that can expand into applying the learned facts to a given problem.

What I've seen these models do are swarm-problem solving, defining a rudementary language between units and the different types of optimizations in learning to fly, walk or survive in the "simulated battle for the cube". All of the tasks were given as "flap the wings to gain maximal efficiency and uplift", or "walk fast", and the programs themselves figured out the details. This type of learner is definately more approachable than just hard-coding a fully functional consciousness.

"Two things are infinite: the universe and human stupidity; and I'm not sure about the universe." - Albert Einstein

Daealis wrote:I think the evolutionary models are the key. Start with a simple learning model that can expand into applying the learned facts to a given problem.

What I've seen these models do are swarm-problem solving, defining a rudementary language between units and the different types of optimizations in learning to fly, walk or survive in the "simulated battle for the cube". All of the tasks were given as "flap the wings to gain maximal efficiency and uplift", or "walk fast", and the programs themselves figured out the details. This type of learner is definately more approachable than just hard-coding a fully functional consciousness.

This is actually a machine learning model, not an evolutionary one (well, the learning could be done via evolutionary). The problem with machine learning is it's hard, you have to know what parameters you're optimizing, or the order in which you're optimizing it... If you give a robot dual cameras and just tell it to learn how to get from point A to point B around obstacles, it has to learn on as much as 14 million variables; if instead you teach a robot to translate those 14 million variables into 6 to 12 bins (vertical columns) and THEN teach it to make decisions off those bins, the robot learns MUCH faster.

borrofburi wrote:If you give a robot dual cameras and just tell it to learn how to get from point A to point B around obstacles, it has to learn on as much as 14 million variables; if instead you teach a robot to translate those 14 million variables into 6 to 12 bins (vertical columns) and THEN teach it to make decisions off those bins, the robot learns MUCH faster.

I'm aware of this problem that it has, with robots not being able to "guesstimate" but doing it in strict manner that leads to said billion variable "efficiency". But, I still feel that this is a more viable approach than the solid sort of hard-coding the whole thing. The problem being of course that we'd have to give the machine a set of goals, one of which includes a process of optimizing themselves.

One is the said "guesstimate" thing, getting a machine to throw a working solution at first and simply tuning it just "enough", instead of calculating the ultimate value to the point of 500 decimals. I think I've seen an article where they've tried to do this kind of estimating processes. Not sure if it was just an idea never pursued or a real study done.

"Two things are infinite: the universe and human stupidity; and I'm not sure about the universe." - Albert Einstein